Journal of Medicinal Chemistry· 2015Q1
pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures
- 5,474citations
- Q1SCImago
- 2015year
Short summary
A new computational tool, pkCSM, utilizes graph-based signatures to predict key drug pharmacokinetic and toxicity properties, performing comparably to or better than existing methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Developed pkCSM, a novel computational tool for predicting drug ADMET properties.
- Uses graph-based signatures as the basis for predictive models.
- Achieves performance equal to or better than current predictive methods.
- Offers a free, accessible web server for rapid property evaluation.
AI-generated from the title and abstract; the full text is not read.
Abstract
Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.
The authors' abstract, as published at the source. Journal of Medicinal Chemistry, 2015 · DOI ↗
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Field: Computational Theory and Mathematics
Computational Theory and MathematicsComputer Science